一种基于二部图和节点角色划分的社交网络推荐方案

doi:10.3969/j.issn.0372-2112.2017.10.016

电子学报 ›› 2017, Vol. 45 ›› Issue (10): 2425-2433.DOI: 10.3969/j.issn.0372-2112.2017.10.016

一种基于二部图和节点角色划分的社交网络推荐方案

肖云鹏, 刘瀚松, 刘宴兵

重庆邮电大学网络与信息安全技术重庆市工程实验室, 重庆 400065

收稿日期:2016-06-07 修回日期:2017-04-09 出版日期:2017-10-25
- 作者简介:
- 肖云鹏,男.1979年8月出生,安徽蚌埠人.副教授、硕士生导师,主要研究方向为大数据和移动互联网.E-mail:xiaoyp@cqupt.edu.cn;刘瀚松,男.1991年8月出生,四川资阳人.现为重庆邮电大学硕士研究生,主要研究方向为推荐系统.;刘宴兵,男.1971年4月出生,四川遂宁人.教授、博士生导师,主要研究方向为网络分析和网络安全.
- 基金资助:
- 国家973重点基础研究发展计划 (No.2013CB329606）; 国家自然科学基金 (No.61272400）; 重庆市青年人才项目 (No.cstc2013kjrc-qnrc40004）; 教育部-中国移动研究基金 (No.MCM20130351）; 重庆市研究生研究与创新项目 (No.CYS14146）; 重庆市教委科学计划项目 (No.KJ1500425）; 重庆邮电大学文峰基金 (No.WF201403）

A Social Network Recommendation Scheme Based on Bipartite Graph and Node Role Division

XIAO Yun-peng, LIU Han-song, LIU Yan-bing

Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2016-06-07 Revised:2017-04-09 Online:2017-10-25 Published:2017-10-25
- Supported by:
- National Program on Key Basic Research Project of China (973 Program) (No.2013CB329606); National Natural Science Foundation of China (No.61272400); Young Talents Program of Chongqing Municipality (No.cstc2013kjrc-qnrc40004); Ministry of Education - China Mobile Research Fund (No.MCM20130351); Chongqing Graduate Research and Innovation Project (No.CYS14146); Science Program of Chongqing Education Commission (No.KJ1500425); Wenfeng Foundation of Chongqing University of Posts and Telecommunications (No.WF201403)

摘要/Abstract

摘要： 针对现有社交网络用户推荐方案中大规模网络个体相似性计算复杂度高以及个体节点无差异对待的问题，本文提出一种基于二部图和节点角色划分的推荐方案.首先，通过划分重叠群体简化原生社交网络结构，并进一步构建群体-个体二部图模型；其次，通过群体-个体二部图所反映的拓扑特征，结合节点自身属性特征，对个体进行角色划分，提出一种基于群体-个体二部图的角色划分模型；最后，针对大规模网络中计算个体相似性复杂度高的问题，构建基于角色差异下的个体-个体二部图模型，实现层次化、个性化的推荐.实验表明，该方案适用于对社交网络中兴趣广泛度存在差异的个体间进行好友推荐，并在较小规模的二部图上生成目标个体推荐列表，降低了计算个体相似性的复杂度.

关键词: 社交网络, 二部图, 角色划分, 个体推荐

Abstract: In the view of the high complexity about similarity calculation and the indifference about individual nodes,a social network recommendation scheme based on bipartite graph and node role division is presented in this study.Firstly,the native social network structure is simplified by dividing overlapping groups.Furthermore,the bipartite graph model of group and individual is given.Secondly,the role division model is proposed by combining topological features of bipartite graph with node attributes.Finally,in order to resolve high computational complexity,the individual bipartite graph model is constructed based on user role difference.The model implements a hierarchical and personalized recommendation.Experiments show that the scheme can effectively recommend among social users who have different interests.In addition,the complexity of individual similarity computation is reduced because the target individual recommendation list is generated based on small scale bipartite graph.

Key words: social network, bipartite graph, role division, individual recommendation

中图分类号:

TP393

肖云鹏, 刘瀚松, 刘宴兵. 一种基于二部图和节点角色划分的社交网络推荐方案[J]. 电子学报, 2017, 45(10): 2425-2433.

XIAO Yun-peng, LIU Han-song, LIU Yan-bing . A Social Network Recommendation Scheme Based on Bipartite Graph and Node Role Division[J]. Acta Electronica Sinica, 2017, 45(10): 2425-2433.

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一种基于二部图和节点角色划分的社交网络推荐方案

A Social Network Recommendation Scheme Based on Bipartite Graph and Node Role Division

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